Advanced ADX[Intellection]█ OVERVIEW
"ADX" is a popular technical analysis indicator used to determine trend strength.
Advanced ADX is divided in two main sectors:
Default ADX
Higher time frame ADX analysis and trend phase
█ DESCRIPTION
You have two ADX's, One has the same time frame as your chart and the other one can be set by yourself in settings, Named Vision time frame.
Default value of "Vision ADX" is on 240minutes means 4hour, We recommend for time frames less than 1h using 4h "Vision time frame".
"Vision main plot" is also based on higher time frame analysis. The higher time frame analysis uses a combination of Three exponential moving averages (67, 89 and 111 periods) and the ADX to determine the position for long or short trades. The "Vision main plot" is shaded and changes color:
Green means bull phase
Red means bear phase
Gray means not defined or neutral
█ TRADING GUIDES
You can filter your signals based on "Vision ADX" value and color
Some trading tips:
When in green zone we don't recommend going short or just lower your risk for short positions. Simply for when ever your position is opposite of the color.
When ADX stays for a long period under 30 then it crosses 30 you might consider a volatility is about to come!
Good volatilities come when there is huge distance between default "ADX" and "Vision ADX"
█ Recap
"Advanced ADX" indicates three analysis:
1-Indicates default "ADX" based on your time frame.
2-Indicates higher time frame "ADX" based on the time frame you choose in settings.
3-Indicates higher time frame trend phase.
Don't forget to take time and learn it before trading it.
Осцилляторы
Limited Fisher Transformwhat is Limited Fisher Transform?
This indicator is a compressed version of the Fisher transform indicator between 100 and 0 values.
what it does?
It allows us to define overbought and oversold zones by compressing the values of the "fisher transform" indicator between 0 and 100. also these zones are the same for every timeframe and trading pair, just like RSI.
how it does it?
it use this formula:
x = fisher transform values
a = average
how to use it?
its use is indistinguishable from the standard fisher. You can use it to set alarms for overbought and oversold zones. so you will be notified when a possible opportunity arises in the market.
RSI Multi Symbol/Time Frame DetectorThis code is an implementation of the Relative Strength Index (RSI) indicator, which is a popular momentum indicator used in technical analysis. The RSI measures the strength of an asset's price action and provides information on whether the asset is overbought or oversold. The code also calculates a moving average of the RSI and allows the user to choose the type of moving average to be calculated (SMA, EMA, SMMA, WMA, or VWMA).
The user can select from different time frames (5, 15, 60, or 240), symbols (SP:SPX, OANDA:EURUSD, or OANDA:NZDUSD), RSI lengths, and moving average types and lengths.
The code starts by defining a function called "ma" for calculating different types of moving averages. This function takes as input the source data for the moving average calculation (the RSI), the length of the moving average, and the type of moving average. The function uses a switch statement to return the appropriate calculation based on the inputted moving average type.
Next, the code calculates the RSI and its moving average. The RSI is calculated using the well-known formula for the RSI, which involves calculating the average gains and losses over a specified period of time and then dividing the average gains by the average losses. The moving average is calculated using the "ma" function defined earlier.
Finally, the code allows the user to choose the symbol and time frame to be used in the RSI calculation, as well as the length of the RSI and the moving average, and the type of moving average. The user can choose from three symbols (SP:SPX, OANDA:EURUSD, OANDA:NZDUSD) and four time frames (5, 15, 60, and 240 minutes). The code then uses the "request.security" function to retrieve the RSI calculation for the selected symbol and time frame.
Note: This code is example for you to use multi timeframe/symbol in your indicator or Strategy , also prevent Repainting Calculation
change in rsiThis indicator will show how fast the rsi of a symbol is changing. you can see this as a differentiation function on rsi .
this will show the change in rsi in percentage.
Ex: suppose the rsi of a symbol at present is 60 and the previous value of rsi was 52,
as you can see the rsi has increased, which is a sign of the symbol being bullish .
this indicator will tell by what percentage the rsi of the symbol has increased or decreased.
for the above example, the change in rsi is 15.38% increase.
this is set to default chart time-frame.
VWOP: Volume Weighted & Oscillated PriceWhile playing around with the standard "ta.vwap" I wondered why there was no length input, so I did some research on what the underlying calculation actually is, and did my best to augment it so as to allow for a variable length based on an oscillator value.
Normal VWAP = (Number of Shares Bought x Typical Price) / Total Volume
In my VWOP Calculation, typical price is replaced by selected moving average type or "matype" and then multiplied by the volume.
Then a total value is calculated using math.sum with a length value that changes according to a selected oscillator's value. The total is then divided by
the sum of just volume using the same oscillating length value. Result is then passed through the selected"matype" once more to give the final result.
Indicator designed for use as a entry/exit indicator in conjunction with more traditional moving averages and/or signal filters. Useful for taking volume + an oscillator into account along with price, instead of just the price as with a simple moving average.
JSS Table - RSI, DI+, DI-, ADXSimple table to show the values for indicators which can be used to initiate trades:
RSI: Long above 55 // Short below 45 // Choppy between 45-55
DI+: Long above 25
DI-: Short above 25
Note when to avoid trend trades:
- If DI+ and DI- are both below 25 then market is choppy
- If RSI is between 45-55 then market is choppy
Hurst Spectral Analysis Oscillator"It is a true fact that any given time history of any event (including the price history of a stock) can always be considered as reproducible to any desired degree of accuracy by the process of algebraically summing a particular series of sine waves. This is intuitively evident if you start with a number of sine waves of differing frequencies, amplitudes, and phases, and then sum them up to get a new and more complex waveform." (Spectral Analysis chapter of J M Hurst's book, Profit Magic )
Background: A band-pass filter or bandpass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outside that range. Bandpass filters are widely used in wireless transmitters and receivers. Well-designed bandpass filters (having the optimum bandwidth) maximize the number of signal transmitters that can exist in a system while minimizing the interference or competition among signals. Outside of electronics and signal processing, other examples of the use of bandpass filters include atmospheric sciences, neuroscience, astronomy, economics, and finance.
About the indicator: This indicator will accept float/decimal length inputs to display a spectrum of 11 bandpass filters. The trader can select a single bandpass for analysis that includes future high/low predictions. The trader can also select which bandpasses contribute to a composite model of expected price action.
10 Statements to describe the 5 elements of Hurst's price-motion model:
Random events account for only 2% of the price change of the overall market and of individual issues.
National and world historical events influence the market to a negligible degree.
Foreseeable fundamental events account for about 75% of all price motion. The effect is smooth and slow changing.
Unforeseeable fundamental events influence price motion. They occur relatively seldom, but the effect can be large and must be guarded against.
Approximately 23% of all price motion is cyclic in nature and semi-predictable (basis of the "cyclic model").
Cyclicality in price motion consists of the sum of a number of (non-ideal) periodic cyclic "waves" or "fluctuations" (summation principle).
Summed cyclicality is a common factor among all stocks (commonality principle).
Cyclic component magnitude and duration fluctuate slowly with the passage of time. In the course of such fluctuations, the greater the magnitude, the longer the duration and vice-versa (variation principle).
Principle of nominality: an element of commonality from which variation is expected.
The greater the nominal duration of a cyclic component, the larger the nominal magnitude (principle of proportionality).
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 DavidF at Sigma-L, and @HPotter
👏 @Saviolis, parisboy, and @upslidedown
ADX Volume Trend
Thie indicator is a modified and upgraded version of the popular ADX tool.
ADX is used to determine the strength of a trend, and also to determine the direction in which the trend is likely to go.
With this script, I have added in the formula the usage of volume, leading to the following functionality.
The length is used to determine the period to calculate the trend strength and direction, and the average is used to then determine the oscillator and to confront the previous line.
The volume average determines how many volumes bars the indicator should use to determine if a volume bar is above or below average if volume mode is selected.
With the volume mode on, you'll get the DI+ and DI- lines, which are by default displayed as a histogram that calculates the difference between the two lines, called "Directional difference", are calculated using also the volume in the formula, multiplying the normal output by the volume multiplier. I suggest using this mode in high-volume markets.
The trend strength difference is the area calculated using the difference between the ADX line and his moving average and can be used to analyze divergences in the swing points.
It has a lot of improvements and new functionalities, like:
- Histogram to show the output at best
- Averages to compare the data
- The option to include the volume inside of the formula
- Other options and esthetic changes
This indicator is created to improve the usability of the popular ADX indicator, including the very important variable of the volumes, in fact, it's the best to use for the Volume Spread Analysis.
HS,HH,LL,and EMA by: rpalconitHello everyone,
HS,HH,LL, and EMA stands for Hull Suite, Higher High, Lower Low and Exponential Moving Average.
Signal Features:
• Long Position: If the Higher High and Lower Low signals are LL and LH at the SUPPORT LEVEL, plot the Fibonacci Retracement and get retracement from 0.382,0.5 and 0.618 for EP. and your SL should be at 1.1 level of the Fibonacci, target TP should be 1.5 ratio. For confirmations the Hull Suite (HS) should be green color and on or below the Exponential Moving Average (EMA).
• Short Position: If the Higher High and Lower Low signals are HH and HL at the RESISTANCE LEVEL, plot the Fibonacci Retracement and get retracement from 0.382,0.5 and 0.618 for EP. and your SL should be at 1.1 level of the Fibonacci, target TP should be 1.5 ratio. For confirmations the Hull Suite (HS) should be red color and on or above the Exponential Moving Average (EMA).
You can change EMA length in any of your preference. The Default is 50.
Details about the indicator
INPUTS
Time Frame
• Time Frames Chart: You can select your preferred timeframe at the dropdown list. Default is 4H. Aside from Time Fame, I advice not to change anything at input default for better result.
STYLE
• Note: For effective signals results and to minimize noise, you need to uncheck first on the style tab: MHULL, BAR COLOR AND LINES.
Best regards,
ruelpalconit
Moving Average Cross and RSIThis is the updated version of the MAC cross Short/ Long indicator i had posted earlier in 2022.
This script includes a RSI and EMA of the RSI with fixed OB and OS Levels.
The purpose is to refine the amount of trades taken from the moving average cross on the 30 minute timeframe.
In the overlay, the red and green dots indicate weather the moving cross is a long or a short signal.
The theory when back tested is:
When the short signal is given, the EMA must be below 30 to enter a short.
When the long signal is given, the EMA must be above 64 to enter a long, anything in between is a false signal.
Only the first dot is meant to be a long or a short signal, not meant to be interpreted as being consecutive.
The data window is meant to be built in a way to easily set up indicators or strategies using Tradelab.ai software.
[@btc_charlie] Trader XO Macro Trend ScannerWhat is this script?
This script has two main functions focusing on EMAs (Exponential Moving Average) and Stochastic RSI.
EMAs
EMAs are typically used to give a view of bullish / bearish momentum. When the shorter EMA (calculated off more recent price action) crosses, or is above, the slower moving EMA (calculated off a longer period of price action), it suggests that the market is in an uptrend. This can be an indication to either go long on said asset, or that it is more preferable to take long setups over short setups. Invalidation on long setups is usually found via price action (e.g. previous lows) or simply waiting for an EMA cross in the opposite direction (i.e. shorter EMA crosses under longer term EMA).
This is not a perfect system for trade entry or exit, but it does give a good indication of market trends. The settings for the EMAs can be changed based on user inputs, and by default the candles are coloured based on the crosses to make it more visual. The default settings are based on “Trader XO’s” settings who is an exceptional swing trader.
RSI
Stochastic RSI is a separate indicator that has been added to this script. RSI measures Relative Strength (RSI = Relative Strength Index). When RSI is <20 it is considered oversold, and when >80 it is overbought. These conditions suggests that momentum is very strong in the direction of the trend.
If there is a divergence between the price (e.g. price is creating higher highs, and stoch RSI is creating lower highs) it suggests the strength of the trend is weakening. Whilst this script does not highlight divergences, what it does highlight is when the shorter term RSI (K) crosses over D (the average of last 3 periods). This can give an indication that the trend is losing strength.
Combination
The EMAs indicate when trend shifts (bullish or bearish).
The RSI indicates when the trend is losing momentum.
The combination of the two can be used to suggest when to prefer a directional bias, and subsequently shift in anticipation of a trend reversal.
Note that no signal is 100% accurate and an interpretation of market conditions and price action will need to be overlayed to
Why is it different to others?
I have not found other scripts that are available in this way visually including alerts when Stoch RSI crosses over/under the extremes; or the mid points.
Whilst these indicators are default, the combination of them and how they are presented is not and makes use of the TradingView colouring functionalities.
What are the features?
Customise the variables (averages) used in the script.
Display as one EMA or two EMAs (the crossing ones).
Alerts on EMA crosses.
Alerts on Stoch RSI crosses - slow/fast, upper, lower areas.
- Currently set on the chart to show alerts when Stoch RSI is above 80, then falls below 80 (and colours it red).
Customisable colours.
What are the best conditions for this?
It is designed for high timeframe charts and analysis in crypto, since crypto tends to trend.
It can however be used for lower timeframes.
Disclaimer/Notes:
I have noticed several videos appearing suggesting that this is a "100% win rate indicator" .
NO indicator has 100% win rate.
An indicator is an *indicator* that is all.
Please use responsibly and let me know if there are any mods or updates you would like to see.
DEVIATION OF THE STOCHASTIC INDICATORThis new technical indicator uses the stochastic oscillator as its base and calculates the deviation of its moving average, generating an alternative view of market volatility.
Any Oscillator Underlay [TTF]We are proud to release a new indicator that has been a while in the making - the Any Oscillator Underlay (AOU) !
Note: There is a lot to discuss regarding this indicator, including its intent and some of how it operates, so please be sure to read this entire description before using this indicator to help ensure you understand both the intent and some limitations with this tool.
Our intent for building this indicator was to accomplish the following:
Combine all of the oscillators that we like to use into a single indicator
Take up a bit less screen space for the underlay indicators for strategies that utilize multiple oscillators
Provide a tool for newer traders to be able to leverage multiple oscillators in a single indicator
Features:
Includes 8 separate, fully-functional indicators combined into one
Ability to easily enable/disable and configure each included indicator independently
Clearly named plots to support user customization of color and styling, as well as manual creation of alerts
Ability to customize sub-indicator title position and color
Ability to customize sub-indicator divider lines style and color
Indicators that are included in this initial release:
TSI
2x RSIs (dubbed the Twin RSI )
Stochastic RSI
Stochastic
Ultimate Oscillator
Awesome Oscillator
MACD
Outback RSI (Color-coding only)
Quick note on OB/OS:
Before we get into covering each included indicator, we first need to cover a core concept for how we're defining OB and OS levels. To help illustrate this, we will use the TSI as an example.
The TSI by default has a mid-point of 0 and a range of -100 to 100. As a result, a common practice is to place lines on the -30 and +30 levels to represent OS and OB zones, respectively. Most people tend to view these levels as distance from the edges/outer bounds or as absolute levels, but we feel a more way to frame the OB/OS concept is to instead define it as distance ("offset") from the mid-line. In keeping with the -30 and +30 levels in our example, the offset in this case would be "30".
Taking this a step further, let's say we decided we wanted an offset of 25. Since the mid-point is 0, we'd then calculate the OB level as 0 + 25 (+25), and the OS level as 0 - 25 (-25).
Now that we've covered the concept of how we approach defining OB and OS levels (based on offset/distance from the mid-line), and since we did apply some transformations, rescaling, and/or repositioning to all of the indicators noted above, we are going to discuss each component indicator to detail both how it was modified from the original to fit the stacked-indicator model, as well as the various major components that the indicator contains.
TSI:
This indicator contains the following major elements:
TSI and TSI Signal Line
Color-coded fill for the TSI/TSI Signal lines
Moving Average for the TSI
TSI Histogram
Mid-line and OB/OS lines
Default TSI fill color coding:
Green : TSI is above the signal line
Red : TSI is below the signal line
Note: The TSI traditionally has a range of -100 to +100 with a mid-point of 0 (range of 200). To fit into our stacking model, we first shrunk the range to 100 (-50 to +50 - cut it in half), then repositioned it to have a mid-point of 50. Since this is the "bottom" of our indicator-stack, no additional repositioning is necessary.
Twin RSI:
This indicator contains the following major elements:
Fast RSI (useful if you want to leverage 2x RSIs as it makes it easier to see the overlaps and crosses - can be disabled if desired)
Slow RSI (primary RSI)
Color-coded fill for the Fast/Slow RSI lines (if Fast RSI is enabled and configured)
Moving Average for the Slow RSI
Mid-line and OB/OS lines
Default Twin RSI fill color coding:
Dark Red : Fast RSI below Slow RSI and Slow RSI below Slow RSI MA
Light Red : Fast RSI below Slow RSI and Slow RSI above Slow RSI MA
Dark Green : Fast RSI above Slow RSI and Slow RSI below Slow RSI MA
Light Green : Fast RSI above Slow RSI and Slow RSI above Slow RSI MA
Note: The RSI naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Stochastic and Stochastic RSI:
These indicators contain the following major elements:
Configurable lengths for the RSI (for the Stochastic RSI only), K, and D values
Configurable base price source
Mid-line and OB/OS lines
Note: The Stochastic and Stochastic RSI both have a normal range of 0 to 100 with a mid-point of 50, so no rescaling or transformations are done on either of these indicators. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Ultimate Oscillator (UO):
This indicator contains the following major elements:
Configurable lengths for the Fast, Middle, and Slow BP/TR components
Mid-line and OB/OS lines
Moving Average for the UO
Color-coded fill for the UO/UO MA lines (if UO MA is enabled and configured)
Default UO fill color coding:
Green : UO is above the moving average line
Red : UO is below the moving average line
Note: The UO naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Awesome Oscillator (AO):
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the AO calculation
Configurable price source for the moving averages used in the AO calculation
Mid-line
Option to display the AO as a line or pseudo-histogram
Moving Average for the AO
Color-coded fill for the AO/AO MA lines (if AO MA is enabled and configured)
Default AO fill color coding (Note: Fill was disabled in the image above to improve clarity):
Green : AO is above the moving average line
Red : AO is below the moving average line
Note: The AO is technically has an infinite (unbound) range - -∞ to ∞ - and the effective range is bound to the underlying security price (e.g. BTC will have a wider range than SP500, and SP500 will have a wider range than EUR/USD). We employed some special techniques to rescale this indicator into our desired range of 100 (-50 to 50), and then repositioned it to have a midpoint of 50 (range of 0 to 100) to meet the constraints of our stacking model. We then do one final repositioning to place it in the correct position the indicator-stack based on which other indicators are also enabled. For more details on how we accomplished this, read our section "Binding Infinity" below.
MACD:
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the MACD calculation
Configurable price source for the moving averages used in the MACD calculation
Configurable length and calculation method for the MACD Signal Line calculation
Mid-line
Note: Like the AO, the MACD also technically has an infinite (unbound) range. We employed the same principles here as we did with the AO to rescale and reposition this indicator as well. For more details on how we accomplished this, read our section "Binding Infinity" below.
Outback RSI (ORSI):
This is a stripped-down version of the Outback RSI indicator (linked above) that only includes the color-coding background (suffice it to say that it was not technically feasible to attempt to rescale the other components in a way that could consistently be clearly seen on-chart). As this component is a bit of a niche/special-purpose sub-indicator, it is disabled by default, and we suggest it remain disabled unless you have some pre-defined strategy that leverages the color-coding element of the Outback RSI that you wish to use.
Binding Infinity - How We Incorporated the AO and MACD (Warning - Math Talk Ahead!)
Note: This applies only to the AO and MACD at time of original publication. If any other indicators are added in the future that also fall into the category of "binding an infinite-range oscillator", we will make that clear in the release notes when that new addition is published.
To help set the stage for this discussion, it's important to note that the broader challenge of "equalizing inputs" is nothing new. In fact, it's a key element in many of the most popular fields of data science, such as AI and Machine Learning. They need to take a diverse set of inputs with a wide variety of ranges and seemingly-random inputs (referred to as "features"), and build a mathematical or computational model in order to work. But, when the raw inputs can vary significantly from one another, there is an inherent need to do some pre-processing to those inputs so that one doesn't overwhelm another simply due to the difference in raw values between them. This is where feature scaling comes into play.
With this in mind, we implemented 2 of the most common methods of Feature Scaling - Min-Max Normalization (which we call "Normalization" in our settings), and Z-Score Normalization (which we call "Standardization" in our settings). Let's take a look at each of those methods as they have been implemented in this script.
Min-Max Normalization (Normalization)
This is one of the most common - and most basic - methods of feature scaling. The basic formula is: y = (x - min)/(max - min) - where x is the current data sample, min is the lowest value in the dataset, and max is the highest value in the dataset. In this transformation, the max would evaluate to 1, and the min would evaluate to 0, and any value in between the min and the max would evaluate somewhere between 0 and 1.
The key benefits of this method are:
It can be used to transform datasets of any range into a new dataset with a consistent and known range (0 to 1).
It has no dependency on the "shape" of the raw input dataset (i.e. does not assume the input dataset can be approximated to a normal distribution).
But there are a couple of "gotchas" with this technique...
First, it assumes the input dataset is complete, or an accurate representation of the population via random sampling. While in most situations this is a valid assumption, in trading indicators we don't really have that luxury as we're often limited in what sample data we can access (i.e. number of historical bars available).
Second, this method is highly sensitive to outliers. Since the crux of this transformation is based on the max-min to define the initial range, a single significant outlier can result in skewing the post-transformation dataset (i.e. major price movement as a reaction to a significant news event).
You can potentially mitigate those 2 "gotchas" by using a mechanism or technique to find and discard outliers (e.g. calculate the mean and standard deviation of the input dataset and discard any raw values more than 5 standard deviations from the mean), but if your most recent datapoint is an "outlier" as defined by that algorithm, processing it using the "scrubbed" dataset would result in that new datapoint being outside the intended range of 0 to 1 (e.g. if the new datapoint is greater than the "scrubbed" max, it's post-transformation value would be greater than 1). Even though this is a bit of an edge-case scenario, it is still sure to happen in live markets processing live data, so it's not an ideal solution in our opinion (which is why we chose not to attempt to discard outliers in this manner).
Z-Score Normalization (Standardization)
This method of rescaling is a bit more complex than the Min-Max Normalization method noted above, but it is also a widely used process. The basic formula is: y = (x – μ) / σ - where x is the current data sample, μ is the mean (average) of the input dataset, and σ is the standard deviation of the input dataset. While this transformation still results in a technically-infinite possible range, the output of this transformation has a 2 very significant properties - the mean (average) of the output dataset has a mean (μ) of 0 and a standard deviation (σ) of 1.
The key benefits of this method are:
As it's based on normalizing the mean and standard deviation of the input dataset instead of a linear range conversion, it is far less susceptible to outliers significantly affecting the result (and in fact has the effect of "squishing" outliers).
It can be used to accurately transform disparate sets of data into a similar range regardless of the original dataset's raw/actual range.
But there are a couple of "gotchas" with this technique as well...
First, it still technically does not do any form of range-binding, so it is still technically unbounded (range -∞ to ∞ with a mid-point of 0).
Second, it implicitly assumes that the raw input dataset to be transformed is normally distributed, which won't always be the case in financial markets.
The first "gotcha" is a bit of an annoyance, but isn't a huge issue as we can apply principles of normal distribution to conceptually limit the range by defining a fixed number of standard deviations from the mean. While this doesn't totally solve the "infinite range" problem (a strong enough sudden move can still break out of our "conceptual range" boundaries), the amount of movement needed to achieve that kind of impact will generally be pretty rare.
The bigger challenge is how to deal with the assumption of the input dataset being normally distributed. While most financial markets (and indicators) do tend towards a normal distribution, they are almost never going to match that distribution exactly. So let's dig a bit deeper into distributions are defined and how things like trending markets can affect them.
Skew (skewness): This is a measure of asymmetry of the bell curve, or put another way, how and in what way the bell curve is disfigured when comparing the 2 halves. The easiest way to visualize this is to draw an imaginary vertical line through the apex of the bell curve, then fold the curve in half along that line. If both halves are exactly the same, the skew is 0 (no skew/perfectly symmetrical) - which is what a normal distribution has (skew = 0). Most financial markets tend to have short, medium, and long-term trends, and these trends will cause the distribution curve to skew in one direction or another. Bullish markets tend to skew to the right (positive), and bearish markets to the left (negative).
Kurtosis: This is a measure of the "tail size" of the bell curve. Another way to state this could be how "flat" or "steep" the bell-shape is. If the bell is steep with a strong drop from the apex (like a steep cliff), it has low kurtosis. If the bell has a shallow, more sweeping drop from the apex (like a tall hill), is has high kurtosis. Translating this to financial markets, kurtosis is generally a metric of volatility as the bell shape is largely defined by the strength and frequency of outliers. This is effectively a measure of volatility - volatile markets tend to have a high level of kurtosis (>3), and stable/consolidating markets tend to have a low level of kurtosis (<3). A normal distribution (our reference), has a kurtosis value of 3.
So to try and bring all that back together, here's a quick recap of the Standardization rescaling method:
The Standardization method has an assumption of a normal distribution of input data by using the mean (average) and standard deviation to handle the transformation
Most financial markets do NOT have a normal distribution (as discussed above), and will have varying degrees of skew and kurtosis
Q: Why are we still favoring the Standardization method over the Normalization method, and how are we accounting for the innate skew and/or kurtosis inherent in most financial markets?
A: Well, since we're only trying to rescale oscillators that by-definition have a midpoint of 0, kurtosis isn't a major concern beyond the affect it has on the post-transformation scaling (specifically, the number of standard deviations from the mean we need to include in our "artificially-bound" range definition).
Q: So that answers the question about kurtosis, but what about skew?
A: So - for skew, the answer is in the formula - specifically the mean (average) element. The standard mean calculation assumes a complete dataset and therefore uses a standard (i.e. simple) average, but we're limited by the data history available to us. So we adapted the transformation formula to leverage a moving average that included a weighting element to it so that it favored recent datapoints more heavily than older ones. By making the average component more adaptive, we gained the effect of reducing the skew element by having the average itself be more responsive to recent movements, which significantly reduces the effect historical outliers have on the dataset as a whole. While this is certainly not a perfect solution, we've found that it serves the purpose of rescaling the MACD and AO to a far more well-defined range while still preserving the oscillator behavior and mid-line exceptionally well.
The most difficult parts to compensate for are periods where markets have low volatility for an extended period of time - to the point where the oscillators are hovering around the 0/midline (in the case of the AO), or when the oscillator and signal lines converge and remain close to each other (in the case of the MACD). It's during these periods where even our best attempt at ensuring accurate mirrored-behavior when compared to the original can still occasionally lead or lag by a candle.
Note: If this is a make-or-break situation for you or your strategy, then we recommend you do not use any of the included indicators that leverage this kind of bounding technique (the AO and MACD at time of publication) and instead use the Trandingview built-in versions!
We know this is a lot to read and digest, so please take your time and feel free to ask questions - we will do our best to answer! And as always, constructive feedback is always welcome!
VolumeFlowVolume & price have a direct correlation with each other. If the fundamental value changes, the price changes and volume follows. If the technicals change, volume changes and price follows.
Because the relationship between volume and price is so connected, I created a script highlighting important volume flow measurements.
The VolumeFlow indicator combines several volume measurements into 1 indicator.
1) Volume net inflow / outflow
2) Volume total flow change
3) Volume cumulation flow
The VolumeFlow indicator uses a scale from 100 high to -100 low, with the zero level being neutral.
The VolumeFlow indicator has 4 inputs:
1) +Volume-
2) VolumeFast
3) VolumeSlow
4) Accum/Dist
Default inputs:
+Volume-
length = 1, color = + green or - red
VolumeFast
length = 2, color = blue
VolumeSlow
length = 3, color = white
Accum/Dist
length = 5, color = brown
Horizontal lines
length = 100, 50, 0, -50, -100, color = white
* The VolumeFlow indicator uses altered pieces of code from my Options360 FibVIP indicator, Tradingview "Up / down volume" indicator and Tradingview "Accumulation/Distribution" indicator. *
VIX OscillatorThis is my VIX Oscillator indicator.
About it:
This indicator takes the Z-Score of the VIX and of the current ticker you are on and presents them in the format of an oscillator.
Key parts of the indicator:
A diagram of the key elements of the indicator are displayed above.
Purple Line: Represents the Z-Score of the current Ticker.
Blue Line: Represents the Z-Score of the VIX
Green fill line: Represents bullish divergence
Red fill line: Represents bearish divergence
How to use it:
Characteristics for long entries:
- Look for recent bullish divergence (green fill line)
- Look for the ticker line (purple line) to be holding above 0 (neutrality)
- look for a bullish cross (purple line (ticker) crossing over blue line (VIX))
Characteristics for short entries:
- Look for recent Bearish divergence
- Look for the VIX line (blue line) to be holding above 0 and the Ticker
- Look for the ticker line to be holding below 0
- Look for a bearish cross (blue crossing above purple)
Some principles:
The bands represent oversold, overbought and neutral.
0 is absolute neutrality. No bias here.
Anything towards + 2.5 is considered normal, moving towards overbought (2.5 or higher).
Anything towards -2.5 is considered normal, moving towards oversold (-2.5 or lower).
+2.5 or higher is overbought.
-2.5 or lower is oversold.
As always, I have prepared a quick tutorial video for your reference of this indicator:
Please let me know your questions, comments or suggestions about this indicator below.
Thank you for checking it out!
Composite Cosmetic CandlesThis is effectively version 2 of my script "Candle Fill % Meter", with a few different/more options available in a more compact form. Choose between multiple oscillator sources, # of dividing lines, and solid or gradient candle fill. Once again this script is intended for use with hollow candles! This script enables you to see more information with less screen space taken up, not to mention it looks nice. Labels by last bar also toggleable in the settings.
